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Graph Theory and Neuroscience

Brain clue to addiction risk: https://www.telegraphindia.com/1170916/jsp/nation/story_173521.jsp

Summary:

This article addresses a unique research method and finding by a group of doctors at the National Institute of Mental Health and Neuroscience in Bangalore. The article starts by recognizing that it is a well-agreed upon fact in the medical community that children of parents with Alcohol-Use Disorder (AUD) are more likely to display inattentiveness, hyperactivity, impulsivity, and to eventually develop AUD themselves. Like many before, the researchers sought to identify a neurological cause for this tendency. Unlike many before, however, instead of investigating individual parts of the brain and how they differ from a typical brain, the Bangalore team investigated brain behavior on a large scale. Putting 70 subjects under MRI and scanning every two seconds for 5 minutes, the researchers first acquired detailed photos of brain activity in these patients. To analyze these photos, they segmented the brains into 160 regions, and then mapped out interactions between each of the regions. Specifically, the researchers were looking at how much interaction there was between neighboring regions. The study showed that the brains of children of parents with AUD, had less activity between neighboring regions, indicating a less efficient neural network.

Relevance to the Course:

This method of study serves as a great example of a real world application of graph theory. Graph theory is a powerful means to visually represent the relationships between any objects within an overall structure. With that in mind, the analysis of the relationships could be quite easily expressed via a graph, with each region of the brain being represented by a node. We could then apply weak or strong ties between regions of the brain depending on the amount of interaction between the two parts. Using a graph would make the information very digestible and allow for easy comparison between brains of children with or without parents with AUD. The use-cases for such a neural graph likely go far beyond the AUD example, and could bring insight to a variety of medical dispositions.

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